The most predominant killers of Americans are Coronary Artery Disease (CAD), heart-related problems and cardiac disorders. Cardiac electrophysiological response and activity analysis are critical for the management of cardiac disorders in the heart tissue and cells, such as ventricular myocardial ischemia caused by a lack of blood oxygen. The clinical reference standard for evaluating cardiac rhythm and events is the 12-lead electrocardiogram (ECG) or multi-channel intra-cardiac electrograms (ICEG).
Currently, analysis of waveform morphologies and time-domain parameters, such as P wave, QRS complex, ST segment, T wave, etc., are used for identifying cardiac arrhythmia events, such as atrial fibrillation (AF), myocardial ischemia (MI), ventricular tachycardia/fibrillation (VT/VF), and so forth. However, the waveform morphologies and time domain parameter analysis are sometimes subjective and time-consuming, and requires extensive medical expertise and clinical experience for accurate interpretation and proper cardiac rhythm management.
Recent research efforts have started to apply more sophisticated mathematical theories to biomedical signal interpretation, such as frequency analysis, symbolic complexity analysis and signal entropy evaluation. However, cardiac electrophysiological (EP) signals (e.g., ECG and ICEG) vary with time and current signal analysis usually cannot localize the precise malfunction severity and trend of cardiac events (e.g. the myocardial ischemia and infarction), such as cardiac pathology irregularity stages, arrhythmia occurrence, drug delivery evaluation, etc.
Known clinical methods typically focus on overall EP signal voltage amplitude analysis for detecting arrhythmia. These methods may not use all the information provided by the EP signals, such as electrophysiological excitation and response activities during the tissue contraction (depolarization) and tissue reperfusion (repolarization). Cardiac abnormality and arrhythmia related information (e.g., timing, energy, etc.), especially in the early stage, may not be efficiently and effectively extracted and characterized by current clinical approaches.
Many traditional medical methods use signal morphology changes to track early cardiac pathologies. However, minute signal morphology changes may not be clearly visible and it is hard to quantitatively evaluate and quantify, for example, QR slope shape change without R peak and Q peak amplitude voltage changes. In addition, there is no known efficient approach to integrate all cardiac electrophysiological activities from different parts of the heart into one single mathematical calculation.
Further, known clinical evaluations may not be efficiently applicable in some cases. For example, myocardial ischemia (MI) detections usually use the golden standard based on ST segment voltage deviation (e.g., 0.1 mV elevation). There are at least two shortcomings with this golden standard for MI analysis: (A) This standard only works for surface ECG signals, but not for intra-cardiac electrogram (ICEG) signals; and (B) the ST segment deviation (voltage) cannot be utilized as a quantitative method for myocardial ischemia severity diagnosis and characterization.
Usually, surface ECG signal analysis based on multi-channel waveform time domain parameters are utilized for cardiac arrhythmia detection, such as heart rate variability (HRV), cardiac wave morphology, R wave, ST segment and T wave amplitudes, etc. However there are no efficient quantitative methods available for cardiac status detection and characterization (e.g., MI) such as quantitative characterization of severity of ongoing ischemia events with chest pain, discomfort, etc. Additionally, most clinical approaches for cardiac arrhythmia identification based on ECG signals are subjective and need extensive clinical expertise and knowledge for accurate pathology interpretation and proper cardiac rhythm management. Furthermore, current known ischemia event detection algorithms may cause false alarms due to single parameter analysis. For example, the amplitude voltage of the ST segment may not be able to provide the severity level of the ischemia event, and heart rate variability may not be able to provide the arrhythmia urgency level either.